Machine learning inference calls for database query processing

ABSTRACT

Techniques for making machine learning inference calls for database query processing are described. In some embodiments, a method of making machine learning inference calls for database query processing may include generating a first batch of machine learning requests based at least on a query to be performed on data stored in a database service, wherein the query identifies a machine learning service, sending the first batch of machine learning requests to an input buffer of an asynchronous request handler, the asynchronous request handler to generate a second batch of machine learning requests based on the first batch of machine learning requests, and obtaining a plurality of machine learning responses from an output buffer of the asynchronous request handler, the machine learning responses generated by the machine learning service using a machine learning model in response to receiving the second batch of machine learning requests.

BACKGROUND

The field of machine learning has become widely acknowledged as a likelysignificant driver of the future of technology. Organizations everywherenow seek to use machine learning techniques to address a wide variety ofproblems, such as optimizing aspects of their products, processes,customer experience, etc. While the high-level view of machine learningsounds simple—e.g., provide training data to a computer, to allow thecomputer to automatically learn from the training data to generate amodel that can make predictions for other data—implementing machinelearning techniques in practice can be tremendously difficult.

This difficulty is partially due to the underlying algorithmic andmathematical complexities of machine learning algorithms, which aretypically developed by academic researchers or individuals at theforefront of the field. Additionally, it is also difficult to generate,update, and deploy useful models, which can be extremely time andresource consumptive and filled with complexities. Moreover, machinelearning models tend to be extremely focused on particular use cases andoperating environments, and thus any change to the underlyingenvironment or use case may require a complete regeneration of a newmodel. Further, constructing and deploying machine learning technologiesis quite different from traditional software engineering, and requirespractices and architectures different from what traditional softwareengineering development teams are familiar with. While machine learningtechniques provide many benefits to organizations, use of such machinelearning techniques requires significant specialized knowledge that isnot easy to use with traditional data processing using relationaldatabases and other data stores.

BRIEF DESCRIPTION OF DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 is a diagram illustrating an environment for machine learninginference calls for database query processing according to someembodiments.

FIG. 2 is a diagram illustrating an asynchronous request handleraccording to some embodiments.

FIG. 3 is a diagram illustrating an environment for machine learninginference calls for database query processing using a local machinelearning model according to some embodiments.

FIG. 4 is a diagram illustrating an alternative environment for machinelearning inference calls for database query processing using a localmachine learning model according to some embodiments.

FIG. 5 is a diagram illustrating example user interfaces for machinelearning inference calls according to some embodiments.

FIG. 6 is a flow diagram illustrating operations of a method for makingmachine learning inference calls for database query processing accordingto some embodiments.

FIG. 7 is a block diagram of an illustrative operating environment inwhich machine learning models are trained and hosted according to someembodiments.

FIG. 8 illustrates an example provider network environment according tosome embodiments.

FIG. 9 is a block diagram of an example provider network that provides astorage service and a hardware virtualization service to customersaccording to some embodiments.

FIG. 10 is a block diagram illustrating an example computer system thatmay be used in some embodiments.

DETAILED DESCRIPTION

The present disclosure relates to methods, apparatus, systems, andnon-transitory computer-readable storage media for machine learninginference calls for database query processing According to someembodiments, machine learning inference calls can be integrated intodatabase queries to enable the use of machine learning techniqueswithout requiring specialized machine learning knowledge on the part ofusers. In some embodiments, machine learning calls can be integratedinto database queries, such as structure query language (SQL) queries,or other popular query languages used to process structured data,without requiring significant changes to applications, databaseservices, etc.

FIG. 1 is a diagram illustrating an environment for machine learninginference calls for database query processing according to someembodiments. As shown in FIG. 1, a provider network 100 can include adatabase service 112. A user may have structured data which is stored inone or more database instances 110 of database service 112. The data maybe added to the database service by the user, from user device 102 ormay be added from services of provider network 100 or other servicesexternal to provider network 100. This data may be analyzed to obtainuseful information for the user. A part of this analysis may includeusing machine learning techniques to perform inference on the data. Forexample, text data may be extracted from images stored in databaseservice 112, text data may be analyzed to identify sentiments associatedwith snippets of the text data, and/or other specialized models may beused to perform inference on the user's data to obtain information aboutthe data. However, as discussed, use of machine learning techniquesoften requires specialized knowledge and is not well integrated intodata management services, such as database service 112. Embodimentsaddress these issues by providing techniques for making machine learninginference calls for database query processing.

A provider network 100 (or, “cloud” provider network) provides userswith the ability to utilize one or more of a variety of types ofcomputing-related resources such as compute resources (e.g., executingvirtual machine (VM) instances and/or containers, executing batch jobs,executing code without provisioning servers), data/storage resources(e.g., object storage, block-level storage, data archival storage,databases and database tables, etc.), network-related resources (e.g.,configuring virtual networks including groups of compute resources,content delivery networks (CDNs), Domain Name Service (DNS)),application resources (e.g., databases, application build/deploymentservices), access policies or roles, identity policies or roles, machineimages, routers and other data processing resources, etc. These andother computing resources may be provided as services, such as ahardware virtualization service that can execute compute instances, astorage service that can store data objects, etc. The users (or“customers”) of provider networks 100 may utilize one or more useraccounts that are associated with a customer account, though these termsmay be used somewhat interchangeably depending upon the context of use.Users may interact with a provider network 100 across one or moreintermediate networks 106 (e.g., the internet) via one or moreinterface(s) 104, such as through use of application programminginterface (API) calls, via a console implemented as a website orapplication, etc. The interface(s) 104 may be part of, or serve as afront-end to, a control plane 102 of the provider network 100 thatincludes “backend” services supporting and enabling the services thatmay be more directly offered to customers.

For example, a cloud provider network (or just “cloud”) typically refersto a large pool of accessible virtualized computing resources (such ascompute, storage, and networking resources, applications, and services).A cloud can provide convenient, on-demand network access to a sharedpool of configurable computing resources that can be programmaticallyprovisioned and released in response to customer commands. Theseresources can be dynamically provisioned and reconfigured to adjust tovariable load. Cloud computing can thus be considered as both theapplications delivered as services over a publicly accessible network(e.g., the Internet, a cellular communication network) and the hardwareand software in cloud provider data centers that provide those services.

Generally, the traffic and operations of a provider network may broadlybe subdivided into two categories: control plane operations carried overa logical control plane and data plane operations carried over a logicaldata plane. While the data plane represents the movement of user datathrough the distributed computing system, the control plane representsthe movement of control signals through the distributed computingsystem. The control plane generally includes one or more control planecomponents distributed across and implemented by one or more controlservers. Control plane traffic generally includes administrativeoperations, such as system configuration and management (e.g., resourceplacement, hardware capacity management, diagnostic monitoring, systemstate information). The data plane includes customer resources that areimplemented on the provider network (e.g., computing instances,containers, block storage volumes, databases, file storage). Data planetraffic generally includes non-administrative operations such astransferring customer data to and from the customer resources. Thecontrol plane components are typically implemented on a separate set ofservers from the data plane servers, and control plane traffic and dataplane traffic may be sent over separate/distinct networks.

To provide these and other computing resource services, providernetworks 100 often rely upon virtualization techniques. For example,virtualization technologies may be used to provide users the ability tocontrol or utilize compute instances (e.g., a VM using a guest operatingsystem (O/S) that operates using a hypervisor that may or may notfurther operate on top of an underlying host O/S, a container that mayor may not operate in a VM, an instance that can execute on “bare metal”hardware without an underlying hypervisor), where one or multiplecompute instances can be implemented using a single electronic device.Thus, a user may directly utilize a compute instance (e.g., provided bya hardware virtualization service) hosted by the provider network toperform a variety of computing tasks. Additionally, or alternatively, auser may indirectly utilize a compute instance by submitting code to beexecuted by the provider network (e.g., via an on-demand code executionservice), which in turn utilizes a compute instance to execute thecode—typically without the user having any control of or knowledge ofthe underlying compute instance(s) involved.

As shown in FIG. 1, a request can be sent to a database service 112 toperform a query on data stored in one or more database instances 110. Insome embodiments, the request can originate from a user device 102, asshown at numeral 1A, or from a service 108 (e.g., a serverless functionor other service) of provider network 100, as shown at numeral 1B. Invarious embodiments, a “serverless” function may include code providedby a user or other entity—such as the provider network itself—that canbe executed on demand Serverless functions may be maintained withinprovider network 100 by an on-demand code execution service and may beassociated with a particular user or account or be generally accessibleto multiple users/accounts. A serverless function may be associated witha Uniform Resource Locator (URL), Uniform Resource Identifier (URI), orother reference, which may be used to invoke the serverless function. Aserverless function may be executed by a compute instance, such as avirtual machine, container, etc., when triggered or invoked. In someembodiments, a serverless function can be invoked through an applicationprogramming interface (API) call or a specially formatted HyperTextTransport Protocol (HTTP) request message. Accordingly, users can defineserverless functions that can be executed on demand, without requiringthe user to maintain dedicated infrastructure to execute the serverlessfunction. Instead, the serverless functions can be executed on demandusing resources maintained by the provider network 100. In someembodiments, these resources may be maintained in a “ready” state (e.g.,having a pre-initialized runtime environment configured to execute theserverless functions), allowing the serverless functions to be executedin near real-time.

The request may originate from a client 104A executing on user device102, or a client 104B of service 108, which may interface with thedatabase service 112 through one or more interfaces, such as applicationprogramming interfaces (APIs), text interfaces, graphical userinterfaces (GUIs), or other interfaces. The request may include adatabase query, such as a SQL (or other query language) statement.Although embodiments are described generally using SQL statements, thisis for ease of illustration and not intended to be limiting. Embodimentsmay be similarly implemented using alternative query languages. Thedatabase instance 110 can process the query included in the request. Invarious embodiments, the database service can be updated to identifyinference requests included in a database query. In some embodiments,the database service 112 can be updated to be able to identify API callsfor APIs published by machine learning-backed service 120. Machinelearning-backed service 120 may include one or more pretrained modelsthat may be used to perform inference on user data. The models may betrained for various inference tasks that may be used by multiple users,such as sentiment analysis, text identification, object detection, etc.

In some embodiments, a user may train custom models or provide their ownmodels which are then hosted by a machine learning service 116 as hostedmodels 118. These hosted models may be used to perform inference tasksthat are specific to the user, based on the user's own training data, orotherwise user-specific tasks. In such embodiments, the user may createa function, or model invocation command, which the database service willrecognize during query execution. For example, a user may have a hostedmodel 118 that can be used to perform fraud detection on data stored indatabase service 112. To perform inference using the model, the user mayinstruct the database service to recognize when the model is beinginvoked in a query, such as through a user defined function:

create model My_FraudDetection from [storage location] returns [type] ([inputs to the model] );

The user may then use the model to perform inference on data in databaseservice 112 in a database query. For example, such a query may include:select My_FraudDetection ([inputs]) from [data source], where the hostedmodel My_FraudDetection is invoked on data from the data source, such asone or more database tables, particular rows of one or more databasetables, etc., based on the inputs. During query processing, a databaseparser can identify the My_FraudDetection call within the selectstatement and determine data associated with that call to be provided tothe machine learning service to perform inference using the model. Forexample, the [inputs] may include a statement that identifies one ormore columns of a particular database table, or particular row(s) andcolumn(s) of a particular database table, data from multiple databasetables, etc. Similarly, if an API associated with a machinelearning-backed service is identified during query processing, adatabase parser can identify the API within the query and determine dataassociated with the API to be provided to the machine learning-backedservice to perform inference using a pretrained model.

At numeral 2, the data to be provided to the machine learning service orthe machine learning-backed service can be provided to asynchronousrequest handler 114. If each record identified as being associated witha machine learning call is passed to the machine learning service ormachine learning-backed service individually, the resulting delay (e.g.,introduced by the various network calls added by invoking anotherservice and the actual inference time) would lead to a poor userexperience. Instead, the query processing of the database instance 110and the inference performed by the machine learning service 116 ormachine learning-backed service 120 can be decoupled using anasynchronous request handler 114.

The asynchronous request handler can receive the data on which inferenceis to be performed in an input buffer. This enables the database serviceto send machine learning requests in a batch, where the batch mayinclude a number of records up to the input buffer size. When theasynchronous request handler determined data has been added to the inputbuffer, the asynchronous request handler 114 can create a mini-batch ofdata from the input buffer to be sent to the machine learning service orthe machine learning backed service, as shown as numerals 3A and 3B,depending on which service was invoked in the query. The mini-batch sizemay be service-specific, as each service may be configured to receive adifferent maximum number of records at once. For example, the APIsprovided by a given service may place a limit on the number of requestswhich may be included in a batch. The mini-batch size and the inputbuffer batch size may be different, and the asynchronous request handlercan generate a mini-batch from the requests in its input buffer. In someembodiments, the mini-batch size may be smaller than the input bufferbatch size, in which case the asynchronous request handler may generatemultiple mini-batches until all of the machine learning requests fromthe input buffer have been sent to the invoked external service (e.g.,machine learning service or machine learning-backed service). In someembodiments, the mini-batch size may be larger than or equal to theinput buffer batch size, in which case the mini-batch may include all ofthe requests included in the input buffer.

In some embodiments, a single query may not generate enough machinelearning requests to fill the input buffer of the asynchronous requesthandler 114. In such cases, the asynchronous request handler may obtainmachine learning requests generated by multiple queries, includingqueries from different users and/or as part of different transactionsbeing performed by the database service.

In response to receiving a mini-batch of machine learning requests, themachine learning service 116 or machine-learning backed service 120(depending on which service was invoked in the query) can performinference on the records included in the mini-batch and generate aresponse for each record. The response can be added to an output bufferof the asynchronous request handler 114 at numerals 4A or 4B. Theasynchronous request handler can monitor the output buffer and add aflag or other data indicating that a complete set of responses has beenreceived for the mini-batch of requests that was sent. The databaseservice can monitor the output buffer and, when a flag is identified,can pull the responses from the output buffer, as shown at numeral 5. Insome embodiments, where the asynchronous request handler is processingrequests from multiple users and/or transactions each database instancemay monitor the output buffer for its particular responses and pull onlythose responses which correspond to the requests sent by that instance.In some embodiments, each response may identify the request, databaseinstance, user, and/or transaction with which the response isassociated. Query processing may be completed by the database instanceusing the response from the machine learning service and/or the machinelearning-backed service and, at numeral 6A or 6B, the result of thequery can be returned.

FIG. 2 is a diagram illustrating an asynchronous request handleraccording to some embodiments. When a database instance 110 processes aquery it can identify a query execution plan to perform the query. Agiven query can be executed in many different ways, and each way mayoffer different performance characteristics. A query optimizer may bestquery execution plan for a given query, based on one or more performancerequirements for the query. In some embodiments, during query execution,the database processor 208 can create a virtual operator 200 whichenables execution of the query execution plan to add a thread in whichmachine learning request(s) can be sent and response can be receivedwithout blocking a main query processing thread. In some embodiments,the query optimizer can change an evaluation order of the predicates inthe query to reduce the number of records that require machine learningcalls to be made by the virtual operator 200.

Virtual operator 200 can identify records that need to be sent to amachine learning service or machine learning-backed service in batchesequal to the input buffer 202 size of the asynchronous request handler114. In some embodiments, virtual operator 200 may be implemented as atemporary data structure (e.g., temporary file, scratch pad, or otherdata structure) which may be used to perform at least a portion of thequery to identify the records that are to be sent to the machinelearning service or the machine learning-backed service. For example, aquery may specify that data from multiple tables in the database serviceare to be joined and then a portion of the records in the joined datamay be identified to be sent to the machine learning service or themachine learning backed service. By using the virtual operator, machinelearning requests can be identified and sent to the asynchronous requesthandler in parallel to processing other portions of the query. Atnumeral 1, a batch of machine learning requests (e.g., including arecord, a model endpoint/API, etc.) can be sent to the input buffer 202.In some embodiments, a different input buffer may be maintained for eachmachine learning service and machine learning-backed service to whichthe machine learning requests may be sent. Each input buffer may beimplemented as a queue or other data structure to which the requests maybe added by the virtual operator. A batch handler 204 can generatemini-batches of an appropriate size for the service being invoked. Forexample, at numeral 2, batch handler 204 can divide the input batch frominput buffer 202 into multiple mini-batches to be sent to the invokedservice. At numeral 3, each mini-batch can be sent, in turn, to theinvoked external service (e.g., machine learning service 116 or machinelearning-backed service 120). As discussed, in some embodiments, theinput batch size may be smaller than the batch size associated with theinvoked external service. In such instances, the mini-batch may includeall of the machine learning requests from the input batch.

As the machine learning responses are generated, the external servicecan add the results to an output buffer 206 of the asynchronous requesthandler 114, as shown at numeral 4. When each mini-batch has beencompletely processed, the external service can add a flag or otherindicator to the output buffer indicating that processing of themini-batch is complete. In some embodiments, the external service mayadditionally, or alternatively, add a flag or other indicator to theoutput buffer once all machine learning requests associated with a giventransaction have been completed. Database processor 208 can besimultaneously executing the query execution plan while the machinelearning requests and response are obtained in a separate thread. Whenquery execution reaches the machine learning service invocation (e.g.,the API call, user defined function, etc.), the database processor 208can access the output buffer 206 for the machine learning responses, atnumeral 5. If the responses have not yet been populated in the outputbuffering, processing can wait until a flag (or flags) has been set inthe output buffer indicating that processing is complete.

FIG. 3 is a diagram illustrating an environment for machine learninginference calls for database query processing using a local machinelearning model according to some embodiments. As discussed, when usedwith a machine learning service 116 and a hosted model 118, the user candefine a function associated with the hosted model 118 such that thedatabase instance can identify that the machine learning service isbeing invoked. At numeral 1, the user defined function statement can bereceived by the database parser 200 (e.g., a SQL parser or otherparser). In some embodiments, to reduce the number of network callsrequired by the database service, at numeral 2, a request can be sent tothe machine learning service for the model identified in the userdefined function statement. This request can include performance,hardware, or other characteristics of the database instance. The machinelearning service can compile a copy of the model for the databaseinstance and, at numeral 3, return the model to the database instance.In some embodiments, the compiled model 306 may be implemented in ashared library 304.

At numeral 4, a model schema 302 can be generated which maps the invokedmachine learning model to a compiled model 306. Subsequently, at numeral5, a query can be received by the database instance 110 which invokesthe model. The database processor can use the model schema 302 toidentify the corresponding compiled model 306 in the model library 304and direct the machine learning requests to the compiled model via theasynchronous request handler 302, as shown at numeral 6. Processing ofthe machine learning requests and responses may proceed generally asdescribed above with respect to FIG. 2, except instead of sending amini-batch of requests via a network call to an external service, themini-batch of request is sent locally to the compiled model 306 in themodel library 304. This reduces the number of network calls required tothe number of models being used in a given transaction.

FIG. 4 is a diagram illustrating an alternative environment for machinelearning inference calls for database query processing using a localmachine learning model according to some embodiments. In the embodimentof FIG. 4, the database service may be implemented as a plurality ofnodes, including the database instance 110 (e.g., a head node) and aplurality of nodes 400A-400N. The data stored in the database instancemay be spread across the plurality of storage nodes. Numerals 1-3 mayproceed as discussed above with respect to FIG. 3, however at numeral 3the compiled model is received by a model deployer 402. Model deployer402 can obtain the compiled model and, at numeral 4, deploy a copy ofthe model to each storage node 400A-400N.

At numeral 5, a model schema 302 can be generated which maps the invokedmachine learning model to a compiled model 306. Subsequently, at numeral6, a query can be received by the database instance 110 which invokesthe model. At numeral 7, the query can be executed in parallel on one ormore of the storage nodes based on where the data is stored. As thequery is processed in parallel, inference may also be performed inparallel on each storage node using the compiled model 306 identifiedusing the model schema 302. In some embodiments, each node may alsoinclude an asynchronous request handler which can pass batches ofrequests to each compiled model on its corresponding storage node.Processing of the machine learning requests and responses may proceedgenerally as described above with respect to FIG. 2, except instead ofsending a mini-batch of requests via a network call to an externalservice, the mini-batch of request is sent locally to the compiled model306 in the model library 304. This reduces the number of network callsrequired to the number of models being used in a given transaction.

FIG. 5 is a diagram illustrating example user interfaces for machinelearning inference calls according to some embodiments. As shown in FIG.5, multiple user interfaces (UIs) 500 can be implemented to enableinference calls to be made within a given database query. For example,UI 502 can invoke a machine learning service using ML_service function(or other user defined function) on text input (e.g., through the selectstatement identifying, e.g., a column named “review” from a databasetable named “review_table.” Additionally, or alternatively, UI 504 canbe used to perform inference on arbitrary data types, such as files of“file_name” stored at a “storage_location” (e.g., a data store name,URI, URL, or other location identifier) from a dataset, such as adatabase table. Additionally, or alternatively, UI 506 can invoke a userdefined function “ML_function”, which as previously discussed, may bedefined by a user to invoke a particular model (e.g., model_name) toperform inference on records from a dataset based on one or more inputvalues. In some embodiments, the model may be invoked directly, as shownat UI 508, rather than using the user defined function shown in UI 506.In some embodiments, a view style UI 510 can be used to invoke a modelto perform inference on particular records (such as those included intable T1, as shown in FIG. 5, or on other records as defined in apredicate statement) from a predefined view V1.

FIG. 6 is a flow diagram illustrating operations 600 of a method formaking machine learning inference calls for database query processingaccording to some embodiments. Some or all of the operations 600 (orother processes described herein, or variations, and/or combinationsthereof) are performed under the control of one or more computer systemsconfigured with executable instructions and are implemented as code(e.g., executable instructions, one or more computer programs, or one ormore applications) executing collectively on one or more processors, byhardware or combinations thereof. The code is stored on acomputer-readable storage medium, for example, in the form of a computerprogram comprising instructions executable by one or more processors.The computer-readable storage medium is non-transitory. In someembodiments, one or more (or all) of the operations 600 are performed bydatabase instance 110, asynchronous request handler 114, etc. of theother figures.

The operations 600 include, at block 602, executing at least a portionof a query on data stored in a database service using a temporary datastructure to generate a first batch of machine learning requests,wherein the query identifies a machine learning service. In someembodiments, the temporary data structure may be a virtual operatorwhich is created by the database processor to perform all or parts ofthe query. In some embodiments, the query plan identified to execute allor part of the query may be optimized to reduce the number of machinelearning calls that need to be made to process the query. In someembodiments, the query is a structured query language (SQL) query. Insome embodiments, the SQL query identifies the machine learning using anapplication programming interface (API) call to the machine learningservice. In some embodiments, the machine learning service publishes theAPI to perform inference using the machine learning model in response torequests received from a plurality of users. In some embodiments, thequery identifies the machine learning service using an endpointassociated with the machine learning model hosted by the machinelearning service.

The operations 600 further include, at block 604, generating a secondbatch of machine learning requests based on the first batch of machinelearning requests and based on the machine learning service. In someembodiments, the first batch of machine learning requests can be addedto an input buffer of an asynchronous request handler. As discussed, theasynchronous request handler can manage machine learning requests to besent to a machine learning service or a machine learning-backed service.In some embodiments, the second batch of machine learning requests issent to the machine learning service over at least one network. In someembodiments, the second batch size is different from the first batchsize, and wherein the second batch size is associated with the machinelearning service. For example, the machine learning service may have amaximum batch size, which limits the number of requests which may besent in a batch to the machine learning service. In some embodiments,the first batch of machine learning requests includes machine learningrequests generated in response to multiple queries received from aplurality of different users.

In some embodiments, the operations 600 may further include sending arequest to the machine learning service for the machine learning model,receiving the machine learning model from the machine learning service,the machine learning model compiled for the database service by themachine learning service, and wherein the second batch of machinelearning requests is sent to the machine learning model hosted by thedatabase service. In some embodiments, the operations 600 may furtherinclude storing a copy of the machine learning model in a plurality ofnodes of the database service, wherein machine learning requestsgenerated during the query processing by a particular node of thedatabase service are sent to the copy of the machine learning modelstored on the particular node.

The operations 600 further include, at block 606, obtaining a pluralityof machine learning responses, the machine learning responses generatedby the machine learning service using a machine learning model inresponse to receiving the second batch of machine learning requests. Insome embodiments, as discussed, the plurality of machine learningresponses may be added to an output buffer of the asynchronous requesthandler. The database processor may obtain the machine learningresponses from the output buffer and use the responses to completeprocessing of the query.

In some embodiments, the operations 600 may include receiving a requestat a database service, wherein the request includes a structured querylanguage (SQL) query to be performed on at least a portion of a datasetin the database service and wherein the request identifies a machinelearning service to be used in processing the SQL query, creating avirtual operator to perform at least a portion of the SQL query,generating a first batch of machine learning requests based at least onthe portion of the SQL query performed by the virtual operator, sendingthe first batch of machine learning requests to an input buffer of anasynchronous request handler, the asynchronous request handler togenerate a second batch of machine learning requests based on the firstbatch of machine learning requests, obtaining a plurality of machinelearning responses from an output buffer of the asynchronous requesthandler, the machine learning responses generated by the machinelearning service using a machine learning model in response to receivingthe second batch of machine learning requests, and generating a queryresponse based on the machine learning responses.

In some embodiments, generating a first batch of machine learningrequests based at least on the SQL query, further comprises determininga query execution plan that minimizes a number of records associatedwith machine learning request. In some embodiments, the machine learningservice adds a flag to the output buffer when the second batch ofmachine learning requests has been processed.

FIG. 7 is a block diagram of an illustrative operating environment inwhich machine learning models are trained and hosted according to someembodiments. The operating environment includes end user devices 102, amodel training system 700, a model hosting system 702, a training datastore 760, a training metrics data store 765, a container data store770, a training model data store 775, and a model prediction data store780.

A machine learning service 116 described herein may include one or moreof these entities, such as the model hosting system 702, model trainingsystem 702, and so forth.

In some embodiments, users, by way of user devices 102, interact withthe model training system 702 to provide data that causes the modeltraining system 702 to train one or more machine learning models, forexample, as described elsewhere herein. A machine learning model,generally, may be thought of as one or more equations that are “trained”using a set of data. In some embodiments, the model training system 702provides ML functionalities as a web service, and thus messaging betweenuser devices 102 and the model training system 702 (or provider network100), and/or between components of the model training system 702 (orprovider network 100), can use HTTP messages to transfer data in amachine-readable file format, such as eXtensible Markup Language (XML)or JavaScript Object Notation (JSON). In some embodiments, providingaccess to various functionality as a web service is not limited tocommunications exchanged via the World Wide Web and more generallyrefers to a service that can communicate with other electronic devicesvia a computer network.

The user devices 102 can interact with the model training system 702 viafrontend 729 of the model training system 702. For example, a userdevices 102 can provide a training request to the frontend 729 thatincludes a container image (or multiple container images, or anidentifier of one or multiple locations where container images arestored), an indicator of input data (for example, an address or locationof input data), one or more hyperparameter values (for example, valuesindicating how the algorithm will operate, how many algorithms to run inparallel, how many clusters into which to separate data, and so forth),and/or information describing the computing machine on which to train amachine learning model (for example, a graphical processing unit (GPU)instance type, a central processing unit (CPU) instance type, an amountof memory to allocate, a type of virtual machine instance to use fortraining, and so forth).

In some embodiments, the container image can include one or more layers,where each layer represents an executable instruction. Some or all ofthe executable instructions together represent an algorithm that definesa machine learning model. The executable instructions (for example, thealgorithm) can be written in any programming language (for example,Python, Ruby, C++, Java, etc.). In some embodiments, the algorithm ispre-generated and obtained by a user, via the user devices 102, from analgorithm repository (for example, a network-accessible marketplace, adata store provided by a machine learning training service, etc.). Insome embodiments, the algorithm is completely user-generated orpartially user-generated (for example, user-provided code modifies orconfigures existing algorithmic code).

In some embodiments, instead of providing a container image (oridentifier thereof) in the training request, the user devices 102 mayprovide, in the training request, an algorithm written in anyprogramming language. The model training system 702 then packages thealgorithm into a container (optionally with other code, such as a “base”ML algorithm supplemented with user-provided code) that is eventuallyloaded into a virtual machine instance 722 for training a machinelearning model, as described in greater detail below. For example, auser, via a user devices 102, may develop an algorithm/code using anapplication (for example, an interactive web-based programmingenvironment) and cause the algorithm/code to be provided—perhaps as partof a training request (or referenced in a training request)—to the modeltraining system 702, where this algorithm/code may be containerized onits own or used together with an existing container having a machinelearning framework, for example.

In some embodiments, instead of providing a container image in thetraining request, the user devices 102 provides, in the trainingrequest, an indicator of a container image (for example, an indicationof an address or a location at which a container image is stored). Forexample, the container image can be stored in a container data store770, and this container image may have been previously created/uploadedby the user. The model training system 702 can retrieve the containerimage from the indicated location and create a container using theretrieved container image. The container is then loaded into a virtualmachine instance 722 for training a machine learning model, as describedin greater detail below.

The model training system 702 can use the information provided by theuser devices 102 to train a machine learning model in one or morepre-established virtual machine instances 722 in some embodiments. Inparticular, the model training system 702 includes a single physicalcomputing device or multiple physical computing devices that areinterconnected using one or more computing networks (not shown), wherethe physical computing device(s) host one or more virtual machineinstances 722. The model training system 702 can handle the acquisitionand configuration of compute capacity (for example, containers,instances, etc., which are described in greater detail below) based onthe information describing the computing machine on which to train amachine learning model provided by the user devices 102. The modeltraining system 702 can then train machine learning models using thecompute capacity, as is described in greater detail below. The modeltraining system 702 can automatically scale up and down based on thevolume of training requests received from user devices 102 via frontend729, thereby relieving the user from the burden of having to worry aboutover-utilization (for example, acquiring too little computing resourcesand suffering performance issues) or under-utilization (for example,acquiring more computing resources than necessary to train the machinelearning models, and thus overpaying).

In some embodiments, the virtual machine instances 722 are utilized toexecute tasks. For example, such tasks can include training a machinelearning model. As shown in FIG. 7, each virtual machine instance 722includes an operating system (OS) 724, a language runtime 726, and oneor more ML training containers 730. Generally, the ML trainingcontainers 730 are logical units created within a virtual machineinstance using the resources available on that instance and can beutilized to isolate execution of a task from other processes (forexample, task executions) occurring in the instance. In someembodiments, the ML training containers 730 are formed from one or morecontainer images and a top container layer. Each container image mayfurther include one or more image layers, where each image layerrepresents an executable instruction. As described above, some or all ofthe executable instructions together represent an algorithm that definesa machine learning model. Changes made to the ML training containers 730(for example, creation of new files, modification of existing files,deletion of files, etc.) are stored in the top container layer. If a MLtraining container 730 is deleted, the top container layer is alsodeleted. However, the container image(s) that form a portion of thedeleted ML training container 730 can remain unchanged. The ML trainingcontainers 730 can be implemented, for example, as Linux containers(LXC), Docker containers, and the like.

The ML training containers 730 may include individual a runtime 734,code 737, and dependencies 732 needed by the code 737 in someembodiments. The runtime 734 can be defined by one or more executableinstructions that form at least a portion of a container image that isused to form the ML training container 730 (for example, the executableinstruction(s) in the container image that define the operating systemand/or runtime to run in the container formed from the container image).The code 737 includes one or more executable instructions that form atleast a portion of a container image that is used to form the MLtraining container 730. For example, the code 737 includes theexecutable instructions in the container image that represent analgorithm that defines a machine learning model, which may reference (orutilize) code or libraries from dependencies 732. The runtime 734 isconfigured to execute the code 737 in response to an instruction tobegin machine learning model training Execution of the code 737 resultsin the generation of model data, as described in greater detail below.

In some embodiments, the code 737 includes executable instructions thatrepresent algorithms that define different machine learning models. Forexample, the code 737 includes one set of executable instructions thatrepresent a first algorithm that defines a first machine learning modeland a second set of executable instructions that represent a secondalgorithm that defines a second machine learning model. In someembodiments, the virtual machine instance 722 executes the code 737 andtrains all of the machine learning models. In some embodiments, thevirtual machine instance 722 executes the code 737, selecting one of themachine learning models to train. For example, the virtual machineinstance 722 can identify a type of training data indicated by thetraining request and select a machine learning model to train (forexample, execute the executable instructions that represent an algorithmthat defines the selected machine learning model) that corresponds withthe identified type of training data.

In some embodiments, the runtime 734 is the same as the runtime 726utilized by the virtual machine instance 722. In some embodiments, theruntime 734 is different than the runtime 726 utilized by the virtualmachine instance 722.

In some embodiments, the model training system 702 uses one or morecontainer images included in a training request (or a container imageretrieved from the container data store 770 in response to a receivedtraining request) to create and initialize a ML training container 730in a virtual machine instance 722. For example, the model trainingsystem 702 creates a ML training container 730 that includes thecontainer image(s) and/or a top container layer.

Prior to beginning the training process, in some embodiments, the modeltraining system 702 retrieves training data from the location indicatedin the training request. For example, the location indicated in thetraining request can be a location in the training data store 760. Thus,the model training system 702 retrieves the training data from theindicated location in the training data store 760. In some embodiments,the model training system 702 does not retrieve the training data priorto beginning the training process. Rather, the model training system 702streams the training data from the indicated location during thetraining process. For example, the model training system 702 caninitially retrieve a portion of the training data and provide theretrieved portion to the virtual machine instance 722 training themachine learning model. Once the virtual machine instance 722 hasapplied and used the retrieved portion or once the virtual machineinstance 722 is about to use all of the retrieved portion (for example,a buffer storing the retrieved portion is nearly empty), then the modeltraining system 702 can retrieve a second portion of the training dataand provide the second retrieved portion to the virtual machine instance722, and so on.

To perform the machine learning model training, the virtual machineinstance 722 executes code 737 stored in the ML training container 730in some embodiments. For example, the code 737 includes some or all ofthe executable instructions that form the container image of the MLtraining container 730 initialized therein. Thus, the virtual machineinstance 722 executes some or all of the executable instructions thatform the container image of the ML training container 730 initializedtherein to train a machine learning model. The virtual machine instance722 executes some or all of the executable instructions according to thehyperparameter values included in the training request. As anillustrative example, the virtual machine instance 722 trains a machinelearning model by identifying values for certain parameters (forexample, coefficients, weights, centroids, etc.). The identified valuesdepend on hyperparameters that define how the training is performed.Thus, the virtual machine instance 722 can execute the executableinstructions to initiate a machine learning model training process,where the training process is run using the hyperparameter valuesincluded in the training request. Execution of the executableinstructions can include the virtual machine instance 722 applying thetraining data retrieved by the model training system 702 as inputparameters to some or all of the instructions being executed.

In some embodiments, executing the executable instructions causes thevirtual machine instance 722 (for example, the ML training container730) to generate model data. For example, the ML training container 730generates model data and stores the model data in a file system of theML training container 730. The model data includes characteristics ofthe machine learning model being trained, such as a number of layers inthe machine learning model, hyperparameters of the machine learningmodel, coefficients of the machine learning model, weights of themachine learning model, and/or the like. In particular, the generatedmodel data includes values for the characteristics that define a machinelearning model being trained. In some embodiments, executing theexecutable instructions causes a modification to the ML trainingcontainer 730 such that the model data is written to the top containerlayer of the ML training container 730 and/or the container image(s)that forms a portion of the ML training container 730 is modified toinclude the model data.

The virtual machine instance 722 (or the model training system 702itself) pulls the generated model data from the ML training container730 and stores the generated model data in the training model data store775 in an entry associated with the virtual machine instance 722 and/orthe machine learning model being trained. In some embodiments, thevirtual machine instance 722 generates a single file that includes modeldata and stores the single file in the training model data store 775. Insome embodiments, the virtual machine instance 722 generates multiplefiles during the course of training a machine learning model, where eachfile includes model data. In some embodiments, each model data fileincludes the same or different model data information (for example, onefile identifies the structure of an algorithm, another file includes alist of coefficients, etc.). The virtual machine instance 722 canpackage the multiple files into a single file once training is completeand store the single file in the training model data store 775.Alternatively, the virtual machine instance 722 stores the multiplefiles in the training model data store 775. The virtual machine instance722 stores the file(s) in the training model data store 775 while thetraining process is ongoing and/or after the training process iscomplete.

In some embodiments, the virtual machine instance 722 regularly storesmodel data file(s) in the training model data store 775 as the trainingprocess is ongoing. Thus, model data file(s) can be stored in thetraining model data store 775 at different times during the trainingprocess. Each set of model data files corresponding to a particular timeor each set of model data files present in the training model data store775 as of a particular time could be checkpoints that representdifferent versions of a partially-trained machine learning model duringdifferent stages of the training process. Accordingly, before trainingis complete, a user, via the user devices 102 can submit a deploymentand/or execution request in a manner as described below to deploy and/orexecute a version of a partially trained machine learning model (forexample, a machine learning model trained as of a certain stage in thetraining process). A version of a partially-trained machine learningmodel can be based on some or all of the model data files stored in thetraining model data store 775.

In some embodiments, a virtual machine instance 722 executes code 737stored in a plurality of ML training containers 730. For example, thealgorithm included in the container image can be in a format that allowsfor the parallelization of the training process. Thus, the modeltraining system 702 can create multiple copies of the container imageprovided in a training request and cause the virtual machine instance722 to load each container image copy in a separate ML trainingcontainer 730. The virtual machine instance 722 can then execute, inparallel, the code 737 stored in the ML training containers 730. Thevirtual machine instance 722 can further provide configurationinformation to each ML training container 730 (for example, informationindicating that N ML training containers 730 are collectively training amachine learning model and that a particular ML training container 730receiving the configuration information is ML training container 730number X of N), which can be included in the resulting model data. Byparallelizing the training process, the model training system 702 cansignificantly reduce the training time in some embodiments.

In some embodiments, a plurality of virtual machine instances 722execute code 737 stored in a plurality of ML training containers 730.For example, the resources used to train a particular machine learningmodel can exceed the limitations of a single virtual machine instance722. However, the algorithm included in the container image can be in aformat that allows for the parallelization of the training process.Thus, the model training system 702 can create multiple copies of thecontainer image provided in a training request, initialize multiplevirtual machine instances 722, and cause each virtual machine instance722 to load a container image copy in one or more separate ML trainingcontainers 730. The virtual machine instances 722 can then each executethe code 737 stored in the ML training containers 730 in parallel. Themodel training system 702 can further provide configuration informationto each ML training container 730 via the virtual machine instances 722(for example, information indicating that N ML training containers 730are collectively training a machine learning model and that a particularML training container 730 receiving the configuration information is MLtraining container 730 number X of N, information indicating that Mvirtual machine instances 722 are collectively training a machinelearning model and that a particular ML training container 730 receivingthe configuration information is initialized in virtual machine instance722 number Y of M, etc.), which can be included in the resulting modeldata. As described above, by parallelizing the training process, themodel training system 702 can significantly reduce the training time insome embodiments.

In some embodiments, the model training system 702 includes a pluralityof physical computing devices and two or more of the physical computingdevices hosts one or more virtual machine instances 722 that execute thecode 737. Thus, the parallelization can occur over different physicalcomputing devices in addition to over different virtual machineinstances 722 and/or ML training containers 730.

In some embodiments, the model training system 702 includes a ML modelevaluator 728. The ML model evaluator 728 can monitor virtual machineinstances 722 as machine learning models are being trained, obtainingthe generated model data and processing the obtained model data togenerate model metrics. For example, the model metrics can includequality metrics, such as an error rate of the machine learning modelbeing trained, a statistical distribution of the machine learning modelbeing trained, a latency of the machine learning model being trained, aconfidence level of the machine learning model being trained (forexample, a level of confidence that the accuracy of the machine learningmodel being trained is known, etc. The ML model evaluator 728 can obtainthe model data for a machine learning model being trained and evaluationdata from the training data store 760. The evaluation data is separatefrom the data used to train a machine learning model and includes bothinput data and expected outputs (for example, known results), and thusthe ML model evaluator 728 can define a machine learning model using themodel data and execute the machine learning model by providing the inputdata as inputs to the machine learning model. The ML model evaluator 728can then compare the outputs of the machine learning model to theexpected outputs and determine one or more quality metrics of themachine learning model being trained based on the comparison (forexample, the error rate can be a difference or distance between themachine learning model outputs and the expected outputs).

The ML model evaluator 728 periodically generates model metrics duringthe training process and stores the model metrics in the trainingmetrics data store 765 in some embodiments. While the machine learningmodel is being trained, a user, via the user devices 102, can access andretrieve the model metrics from the training metrics data store 765. Theuser can then use the model metrics to determine whether to adjust thetraining process and/or to stop the training process. For example, themodel metrics can indicate that the machine learning model is performingpoorly (for example, has an error rate above a threshold value, has astatistical distribution that is not an expected or desired distribution(for example, not a binomial distribution, a Poisson distribution, ageometric distribution, a normal distribution, Gaussian distribution,etc.), has an execution latency above a threshold value, has aconfidence level below a threshold value)) and/or is performingprogressively worse (for example, the quality metric continues to worsenover time). In response, in some embodiments, the user, via the userdevices 102, can transmit a request to the model training system 702 tomodify the machine learning model being trained (for example, transmit amodification request). The request can include a new or modifiedcontainer image, a new or modified algorithm, new or modifiedhyperparameter(s), and/or new or modified information describing thecomputing machine on which to train a machine learning model. The modeltraining system 702 can modify the machine learning model accordingly.For example, the model training system 702 can cause the virtual machineinstance 722 to optionally delete an existing ML training container 730,create and initialize a new ML training container 730 using some or allof the information included in the request, and execute the code 737stored in the new ML training container 730 to restart the machinelearning model training process. As another example, the model trainingsystem 702 can cause the virtual machine instance 722 to modify theexecution of code stored in an existing ML training container 730according to the data provided in the modification request. In someembodiments, the user, via the user devices 102, can transmit a requestto the model training system 702 to stop the machine learning modeltraining process. The model training system 702 can then instruct thevirtual machine instance 722 to delete the ML training container 730and/or to delete any model data stored in the training model data store775.

As described below, in some embodiments, the model data stored in thetraining model data store 775 is used by the model hosting system 700 todeploy machine learning models. Alternatively or additionally, a userdevice 102 or another computing device (not shown) can retrieve themodel data from the training model data store 775 to implement alearning algorithm in an external device. As an illustrative example, arobotic device can include sensors to capture input data. A user device102 can retrieve the model data from the training model data store 775and store the model data in the robotic device. The model data defines amachine learning model. Thus, the robotic device can provide thecaptured input data as an input to the machine learning model, resultingin an output. The robotic device can then perform an action (forexample, move forward, raise an arm, generate a sound, etc.) based onthe resulting output.

While the virtual machine instances 722 are shown in FIG. 7 as a singlegrouping of virtual machine instances 722, some embodiments of thepresent application separate virtual machine instances 722 that areactively assigned to execute tasks from those virtual machine instances722 that are not actively assigned to execute tasks. For example, thosevirtual machine instances 722 actively assigned to execute tasks aregrouped into an “active pool,” while those virtual machine instances 722not actively assigned to execute tasks are placed within a “warmingpool.” In some embodiments, those virtual machine instances 722 withinthe warming pool can be pre-initialized with an operating system,language runtimes, and/or other software required to enable rapidexecution of tasks (for example, rapid initialization of machinelearning model training in ML training container(s) 730) in response totraining requests.

In some embodiments, the model training system 102 includes a processingunit, a network interface, a computer-readable medium drive, and aninput/output device interface, all of which can communicate with oneanother by way of a communication bus. The network interface can provideconnectivity to one or more networks or computing systems. Theprocessing unit can thus receive information and instructions from othercomputing systems or services (for example, user devices 102, the modelhosting system 700, etc.). The processing unit can also communicate toand from a memory of a virtual machine instance 722 and further provideoutput information for an optional display via the input/output deviceinterface. The input/output device interface can also accept input froman optional input device. The memory can contain computer programinstructions (grouped as modules in some embodiments) that theprocessing unit executes in order to implement one or more aspects ofthe present disclosure.

In some embodiments, the model hosting system 700 includes a singlephysical computing device or multiple physical computing devices thatare interconnected using one or more computing networks (not shown),where the physical computing device(s) host one or more virtual machineinstances 742. The model hosting system 700 can handle the acquisitionand configuration of compute capacity (for example, containers,instances, etc.) based on demand for the execution of trained machinelearning models. The model hosting system 700 can then execute machinelearning models using the compute capacity, as is described in greaterdetail below. The model hosting system 700 can automatically scale upand down based on the volume of execution requests received from userdevices 102 via frontend of the model hosting system 700, therebyrelieving the user from the burden of having to worry aboutover-utilization (for example, acquiring too little computing resourcesand suffering performance issues) or under-utilization (for example,acquiring more computing resources than necessary to run the machinelearning models, and thus overpaying).

In some embodiments, the virtual machine instances 742 are utilized toexecute tasks. For example, such tasks can include executing a machinelearning model. As shown in FIG. 7, each virtual machine instance 742includes an operating system (OS) 744, a language runtime 746, and oneor more ML scoring containers 750. The ML scoring containers 750 aresimilar to the ML training containers 730 in that the ML scoringcontainers 750 are logical units created within a virtual machineinstance using the resources available on that instance and can beutilized to isolate execution of a task from other processes (forexample, task executions) occurring in the instance. In someembodiments, the ML scoring containers 750 are formed from one or morecontainer images and a top container layer. Each container image furtherincludes one or more image layers, where each image layer represents anexecutable instruction. As described above, some or all of theexecutable instructions together represent an algorithm that defines amachine learning model. Changes made to the ML scoring containers 750(for example, creation of new files, modification of existing files,deletion of files, etc.) are stored in the top container layer. If a MLscoring container 750 is deleted, the top container layer is alsodeleted. However, the container image(s) that form a portion of thedeleted ML scoring container 750 can remain unchanged. The ML scoringcontainers 750 can be implemented, for example, as Linux containers.

The ML scoring containers 750 each include a runtime 754, code 756, anddependencies 752 (for example, supporting software such as libraries)needed by the code 756 in some embodiments. The runtime 754 can bedefined by one or more executable instructions that form at least aportion of a container image that is used to form the ML scoringcontainer 750 (for example, the executable instruction(s) in thecontainer image that define the operating system and/or runtime to runin the container formed from the container image). The code 756 includesone or more executable instructions that form at least a portion of acontainer image that is used to form the ML scoring container 750. Forexample, the code 756 includes the executable instructions in thecontainer image that represent an algorithm that defines a machinelearning model, which may reference dependencies 752. The code 756 canalso include model data that represent characteristics of the definedmachine learning model, as described in greater detail below. Theruntime 754 is configured to execute the code 756 in response to aninstruction to begin execution of a machine learning model. Execution ofthe code 756 results in the generation of outputs (for example,predicted results), as described in greater detail below.

In some embodiments, the runtime 754 is the same as the runtime 746utilized by the virtual machine instance 742. In some embodiments,runtime 754 is different than the runtime 746 utilized by the virtualmachine instance 742.

In some embodiments, the model hosting system 700 uses one or morecontainer images included in a deployment request (or a container imageretrieved from the container data store 770 in response to a receiveddeployment request) to create and initialize a ML scoring container 750in a virtual machine instance 742. For example, the model hosting system700 creates a ML scoring container 750 that includes the containerimage(s) and/or a top container layer.

As described above, a user device 102 can submit a deployment requestand/or an execution request to the model hosting system 700 via thefrontend in some embodiments. A deployment request causes the modelhosting system 700 to deploy a trained machine learning model into avirtual machine instance 742. For example, the deployment request caninclude an identification of an endpoint (for example, an endpoint name,such as an HTTP endpoint name) and an identification of one or moretrained machine learning models (for example, a location of one or moremodel data files stored in the training model data store 775).Optionally, the deployment request also includes an identification ofone or more container images stored in the container data store 770.

Upon receiving the deployment request, the model hosting system 700initializes ones or more ML scoring containers 750 in one or more hostedvirtual machine instance 742. In embodiments in which the deploymentrequest includes an identification of one or more container images, themodel hosting system 700 forms the ML scoring container(s) 750 from theidentified container image(s). For example, a container image identifiedin a deployment request can be the same container image used to form anML training container 730 used to train the machine learning modelcorresponding to the deployment request. Thus, the code 756 of the MLscoring container(s) 750 includes one or more executable instructions inthe container image(s) that represent an algorithm that defines amachine learning model. In embodiments in which the deployment requestdoes not include an identification of a container image, the modelhosting system 700 forms the ML scoring container(s) 750 from one ormore container images stored in the container data store 770 that areappropriate for executing the identified trained machine learningmodel(s). For example, an appropriate container image can be a containerimage that includes executable instructions that represent an algorithmthat defines the identified trained machine learning model(s).

The model hosting system 700 further forms the ML scoring container(s)750 by retrieving model data corresponding to the identified trainedmachine learning model(s) in some embodiments. For example, thedeployment request can identify a location of model data file(s) storedin the training model data store 775. In embodiments in which a singlemodel data file is identified in the deployment request, the modelhosting system 700 retrieves the identified model data file from thetraining model data store 775 and inserts the model data file into asingle ML scoring container 750, which forms a portion of code 756. Insome embodiments, the model data file is archived or compressed (forexample, formed from a package of individual files). Thus, the modelhosting system 700 unarchives or decompresses the model data file toobtain multiple individual files and inserts the individual files intothe ML scoring container 750. In some embodiments, the model hostingsystem 700 stores the model data file in the same location as thelocation in which the model data file was stored in the ML trainingcontainer 730 that generated the model data file. For example, the modeldata file initially was stored in the top container layer of the MLtraining container 730 at a certain offset, and the model hosting system700 then stores the model data file in the top container layer of the MLscoring container 750 at the same offset.

In embodiments in which multiple model data files are identified in thedeployment request, the model hosting system 700 retrieves theidentified model data files from the training model data store 775. Themodel hosting system 700 can insert the model data files into the sameML scoring container 750, into different ML scoring containers 750initialized in the same virtual machine instance 742, or into differentML scoring containers 750 initialized in different virtual machineinstances 742. As an illustrative example, the deployment request canidentify multiple model data files corresponding to different trainedmachine learning models because the trained machine learning models arerelated (for example, the output of one trained machine learning modelis used as an input to another trained machine learning model). Thus,the user may desire to deploy multiple machine learning models toeventually receive a single output that relies on the outputs ofmultiple machine learning models.

In some embodiments, the model hosting system 700 associates theinitialized ML scoring container(s) 750 with the endpoint identified inthe deployment request. For example, each of the initialized ML scoringcontainer(s) 750 can be associated with a network address. The modelhosting system 700 can map the network address(es) to the identifiedendpoint, and the model hosting system 700 or another system (forexample, a routing system, not shown) can store the mapping. Thus, auser device 102 can refer to trained machine learning model(s) stored inthe ML scoring container(s) 750 using the endpoint. This allows for thenetwork address of an ML scoring container 750 to change without causingthe user operating the user device 102 to change the way in which theuser refers to a trained machine learning model.

Once the ML scoring container(s) 750 are initialized, the ML scoringcontainer(s) 750 are ready to execute trained machine learning model(s).In some embodiments, the user device 102 transmits an execution requestto the model hosting system 700 via the frontend, where the executionrequest identifies an endpoint and includes an input to a machinelearning model (for example, a set of input data). The model hostingsystem 700 or another system (for example, a routing system, not shown)can obtain the execution request, identify the ML scoring container(s)750 corresponding to the identified endpoint, and route the input to theidentified ML scoring container(s) 750.

In some embodiments, a virtual machine instance 742 executes the code756 stored in an identified ML scoring container 750 in response to themodel hosting system 700 receiving the execution request. In particular,execution of the code 756 causes the executable instructions in the code756 corresponding to the algorithm to read the model data file stored inthe ML scoring container 750, use the input included in the executionrequest as an input parameter, and generate a corresponding output. Asan illustrative example, the algorithm can include coefficients,weights, layers, cluster centroids, and/or the like. The executableinstructions in the code 756 corresponding to the algorithm can read themodel data file to determine values for the coefficients, weights,layers, cluster centroids, and/or the like. The executable instructionscan include input parameters, and the input included in the executionrequest can be supplied by the virtual machine instance 742 as the inputparameters. With the machine learning model characteristics and theinput parameters provided, execution of the executable instructions bythe virtual machine instance 742 can be completed, resulting in anoutput.

In some embodiments, the virtual machine instance 742 stores the outputin the model prediction data store 780. Alternatively or in addition,the virtual machine instance 742 transmits the output to the user device102 that submitted the execution result via the frontend.

In some embodiments, the execution request corresponds to a group ofrelated trained machine learning models. Thus, the ML scoring container750 can transmit the output to a second ML scoring container 750initialized in the same virtual machine instance 742 or in a differentvirtual machine instance 742. The virtual machine instance 742 thatinitialized the second ML scoring container 750 can then execute secondcode 756 stored in the second ML scoring container 750, providing thereceived output as an input parameter to the executable instructions inthe second code 756. The second ML scoring container 750 furtherincludes a model data file stored therein, which is read by theexecutable instructions in the second code 756 to determine values forthe characteristics defining the machine learning model. Execution ofthe second code 756 results in a second output. The virtual machineinstance 742 that initialized the second ML scoring container 750 canthen transmit the second output to the model prediction data store 780and/or the user device 102 via the frontend (for example, if no moretrained machine learning models are needed to generate an output) ortransmit the second output to a third ML scoring container 750initialized in the same or different virtual machine instance 742 (forexample, if outputs from one or more additional trained machine learningmodels are needed), and the above-referenced process can be repeatedwith respect to the third ML scoring container 750.

While the virtual machine instances 742 are shown in FIG. 7 as a singlegrouping of virtual machine instances 742, some embodiments of thepresent application separate virtual machine instances 742 that areactively assigned to execute tasks from those virtual machine instances742 that are not actively assigned to execute tasks. For example, thosevirtual machine instances 742 actively assigned to execute tasks aregrouped into an “active pool,” while those virtual machine instances 742not actively assigned to execute tasks are placed within a “warmingpool.” In some embodiments, those virtual machine instances 742 withinthe warming pool can be pre-initialized with an operating system,language runtimes, and/or other software required to enable rapidexecution of tasks (for example, rapid initialization of ML scoringcontainer(s) 750, rapid execution of code 756 in ML scoringcontainer(s), etc.) in response to deployment and/or execution requests.

In some embodiments, the model hosting system 700 includes a processingunit, a network interface, a computer-readable medium drive, and aninput/output device interface, all of which can communicate with oneanother by way of a communication bus. The network interface can provideconnectivity to one or more networks or computing systems. Theprocessing unit can thus receive information and instructions from othercomputing systems or services (for example, user devices 102, the modeltraining system 702, etc.). The processing unit can also communicate toand from a memory of a virtual machine instance 742 and further provideoutput information for an optional display via the input/output deviceinterface. The input/output device interface can also accept input froman optional input device. The memory can contain computer programinstructions (grouped as modules in some embodiments) that theprocessing unit executes in order to implement one or more aspects ofthe present disclosure.

In some embodiments, the operating environment supports many differenttypes of machine learning models, such as multi arm bandit models,reinforcement learning models, ensemble machine learning models, deeplearning models, and/or the like.

The model training system 702 and the model hosting system 700 depictedin FIG. 7 are not meant to be limiting. For example, the model trainingsystem 702 and/or the model hosting system 700 could also operate withina computing environment having a fewer or greater number of devices thanare illustrated in FIG. 7. Thus, the depiction of the model trainingsystem 702 and/or the model hosting system 700 in FIG. 7 may be taken asillustrative and not limiting to the present disclosure. For example,the model training system 702 and/or the model hosting system 700 orvarious constituents thereof could implement various web servicescomponents, hosted or “cloud” computing environments, and/orpeer-to-peer network configurations to implement at least a portion ofthe processes described herein. In some embodiments, the model trainingsystem 702 and/or the model hosting system 700 are implemented directlyin hardware or software executed by hardware devices and may, forinstance, include one or more physical or virtual servers implemented onphysical computer hardware configured to execute computer-executableinstructions for performing the various features that are describedherein. The one or more servers can be geographically dispersed orgeographically co-located, for instance, in one or more points ofpresence (POPs) or regional data centers.

The frontend 729 processes all training requests received from userdevices 102 and provisions virtual machine instances 722. In someembodiments, the frontend 729 serves as a front door to all the otherservices provided by the model training system 702. The frontend 729processes the requests and makes sure that the requests are properlyauthorized. For example, the frontend 729 may determine whether the userassociated with the training request is authorized to initiate thetraining process.

Similarly, frontend processes all deployment and execution requestsreceived from user devices 102 and provisions virtual machine instances742. In some embodiments, the frontend serves as a front door to all theother services provided by the model hosting system 700. The frontendprocesses the requests and makes sure that the requests are properlyauthorized. For example, the frontend may determine whether the userassociated with a deployment request or an execution request isauthorized to access the indicated model data and/or to execute theindicated machine learning model.

The training data store 760 stores training data and/or evaluation data.The training data can be data used to train machine learning models andevaluation data can be data used to evaluate the performance of machinelearning models. In some embodiments, the training data and theevaluation data have common data. In some embodiments, the training dataand the evaluation data do not have common data. In some embodiments,the training data includes input data and expected outputs. While thetraining data store 760 is depicted as being located external to themodel training system 702 and the model hosting system 700, this is notmeant to be limiting. For example, in some embodiments not shown, thetraining data store 760 is located internal to at least one of the modeltraining system 702 or the model hosting system 700.

In some embodiments, the training metrics data store 765 stores modelmetrics. While the training metrics data store 765 is depicted as beinglocated external to the model training system 702 and the model hostingsystem 700, this is not meant to be limiting. For example, in someembodiments not shown, the training metrics data store 765 is locatedinternal to at least one of the model training system 702 or the modelhosting system 700.

The container data store 770 stores container images, such as containerimages used to form ML training containers 730 and/or ML scoringcontainers 750, that can be retrieved by various virtual machineinstances 722 and/or 742. While the container data store 770 is depictedas being located external to the model training system 702 and the modelhosting system 700, this is not meant to be limiting. For example, insome embodiments not shown, the container data store 770 is locatedinternal to at least one of the model training system 702 and the modelhosting system 700.

The training model data store 775 stores model data files. In someembodiments, some of the model data files are comprised of a singlefile, while other model data files are packages of multiple individualfiles. While the training model data store 775 is depicted as beinglocated external to the model training system 702 and the model hostingsystem 700, this is not meant to be limiting. For example, in someembodiments not shown, the training model data store 775 is locatedinternal to at least one of the model training system 702 or the modelhosting system 700.

The model prediction data store 780 stores outputs (for example,execution results) generated by the ML scoring containers 750 in someembodiments. While the model prediction data store 780 is depicted asbeing located external to the model training system 702 and the modelhosting system 700, this is not meant to be limiting. For example, insome embodiments not shown, the model prediction data store 780 islocated internal to at least one of the model training system 702 andthe model hosting system 700.

While the model training system 702, the model hosting system 700, thetraining data store 760, the training metrics data store 765, thecontainer data store 770, the training model data store 775, and themodel prediction data store 780 are illustrated as separate components,this is not meant to be limiting. In some embodiments, any one or all ofthese components can be combined to perform the functionality describedherein. For example, any one or all of these components can beimplemented by a single computing device, or by multiple distinctcomputing devices, such as computer servers, logically or physicallygrouped together to collectively operate as a server system. Any one orall of these components can communicate via a shared internal network,and the collective system (for example, also referred to herein as amachine learning service) can communicate with one or more of the userdevices 102 via the one or more network(s) 106.

Various example user devices 102 are shown in FIG. 7, including adesktop computer, laptop, and a mobile phone, each provided by way ofillustration. In general, the user devices 102 can be any computingdevice such as a desktop, laptop or tablet computer, personal computer,wearable computer, server, personal digital assistant (PDA), hybridPDA/mobile phone, mobile phone, electronic book reader, set-top box,voice command device, camera, digital media player, and the like. Insome embodiments, the model training system 702 and/or the model hostingsystem 700 provides the user devices 102 with one or more userinterfaces, command-line interfaces (CLI), application programinginterfaces (API), and/or other programmatic interfaces for submittingtraining requests, deployment requests, and/or execution requests. Insome embodiments, the user devices 102 can execute a stand-aloneapplication that interacts with the model training system 702 and/or themodel hosting system 700 for submitting training requests, deploymentrequests, and/or execution requests.

In some embodiments, the network 106 includes any wired network,wireless network, or combination thereof. For example, the network 106may be a personal area network, local area network, wide area network,over-the-air broadcast network (for example, for radio or television),cable network, satellite network, cellular telephone network, orcombination thereof. As a further example, the network 106 may be apublicly accessible network of linked networks, possibly operated byvarious distinct parties, such as the Internet. In some embodiments, thenetwork 106 may be a private or semi-private network, such as acorporate or university intranet. The network 106 may include one ormore wireless networks, such as a Global System for MobileCommunications (GSM) network, a Code Division Multiple Access (CDMA)network, a Long Term Evolution (LTE) network, or any other type ofwireless network. The network 106 can use protocols and components forcommunicating via the Internet or any of the other aforementioned typesof networks. For example, the protocols used by the network 106 mayinclude HTTP, HTTP Secure (HTTPS), Message Queue Telemetry Transport(MQTT), Constrained Application Protocol (CoAP), and the like. Protocolsand components for communicating via the Internet or any of the otheraforementioned types of communication networks are well known to thoseskilled in the art and, thus, are not described in more detail herein.

FIG. 8 illustrates an example provider network (or “service providersystem”) environment according to some embodiments. A provider network800 may provide resource virtualization to customers via one or morevirtualization services 810 that allow customers to purchase, rent, orotherwise obtain instances 812 of virtualized resources, including butnot limited to computation and storage resources, implemented on deviceswithin the provider network or networks in one or more data centers.Local Internet Protocol (IP) addresses 816 may be associated with theresource instances 812; the local IP addresses are the internal networkaddresses of the resource instances 812 on the provider network 800. Insome embodiments, the provider network 800 may also provide public IPaddresses 814 and/or public IP address ranges (e.g., Internet Protocolversion 4 (IPv4) or Internet Protocol version 6 (IPv6) addresses) thatcustomers may obtain from the provider 800.

Conventionally, the provider network 800, via the virtualizationservices 810, may allow a customer of the service provider (e.g., acustomer that operates one or more client networks 850A-850C includingone or more customer device(s) 852) to dynamically associate at leastsome public IP addresses 814 assigned or allocated to the customer withparticular resource instances 812 assigned to the customer. The providernetwork 800 may also allow the customer to remap a public IP address814, previously mapped to one virtualized computing resource instance812 allocated to the customer, to another virtualized computing resourceinstance 812 that is also allocated to the customer. Using thevirtualized computing resource instances 812 and public IP addresses 814provided by the service provider, a customer of the service providersuch as the operator of customer network(s) 850A-850C may, for example,implement customer-specific applications and present the customer'sapplications on an intermediate network 840, such as the Internet. Othernetwork entities 820 on the intermediate network 840 may then generatetraffic to a destination public IP address 814 published by the customernetwork(s) 850A-850C; the traffic is routed to the service provider datacenter, and at the data center is routed, via a network substrate, tothe local IP address 816 of the virtualized computing resource instance812 currently mapped to the destination public IP address 814.Similarly, response traffic from the virtualized computing resourceinstance 812 may be routed via the network substrate back onto theintermediate network 840 to the source entity 820.

Local IP addresses, as used herein, refer to the internal or “private”network addresses, for example, of resource instances in a providernetwork. Local IP addresses can be within address blocks reserved byInternet Engineering Task Force (IETF) Request for Comments (RFC) 1918and/or of an address format specified by IETF RFC 4193 and may bemutable within the provider network. Network traffic originating outsidethe provider network is not directly routed to local IP addresses;instead, the traffic uses public IP addresses that are mapped to thelocal IP addresses of the resource instances. The provider network mayinclude networking devices or appliances that provide network addresstranslation (NAT) or similar functionality to perform the mapping frompublic IP addresses to local IP addresses and vice versa.

Public IP addresses are Internet mutable network addresses that areassigned to resource instances, either by the service provider or by thecustomer. Traffic routed to a public IP address is translated, forexample via 1:1 NAT, and forwarded to the respective local IP address ofa resource instance.

Some public IP addresses may be assigned by the provider networkinfrastructure to particular resource instances; these public IPaddresses may be referred to as standard public IP addresses, or simplystandard IP addresses. In some embodiments, the mapping of a standard IPaddress to a local IP address of a resource instance is the defaultlaunch configuration for all resource instance types.

At least some public IP addresses may be allocated to or obtained bycustomers of the provider network 800; a customer may then assign theirallocated public IP addresses to particular resource instances allocatedto the customer. These public IP addresses may be referred to ascustomer public IP addresses, or simply customer IP addresses. Insteadof being assigned by the provider network 800 to resource instances asin the case of standard IP addresses, customer IP addresses may beassigned to resource instances by the customers, for example via an APIprovided by the service provider. Unlike standard IP addresses, customerIP addresses are allocated to customer accounts and can be remapped toother resource instances by the respective customers as necessary ordesired. A customer IP address is associated with a customer's account,not a particular resource instance, and the customer controls that IPaddress until the customer chooses to release it. Unlike conventionalstatic IP addresses, customer IP addresses allow the customer to maskresource instance or availability zone failures by remapping thecustomer's public IP addresses to any resource instance associated withthe customer's account. The customer IP addresses, for example, enable acustomer to engineer around problems with the customer's resourceinstances or software by remapping customer IP addresses to replacementresource instances.

FIG. 9 is a block diagram of an example provider network that provides astorage service and a hardware virtualization service to customers,according to some embodiments. Hardware virtualization service 920provides multiple computation resources 924 (e.g., VMs) to customers.The computation resources 924 may, for example, be rented or leased tocustomers of the provider network 900 (e.g., to a customer thatimplements customer network 950). Each computation resource 924 may beprovided with one or more local IP addresses. Provider network 900 maybe configured to route packets from the local IP addresses of thecomputation resources 924 to public Internet destinations, and frompublic Internet sources to the local IP addresses of computationresources 924.

Provider network 900 may provide a customer network 950, for examplecoupled to intermediate network 940 via local network 956, the abilityto implement virtual computing systems 992 via hardware virtualizationservice 920 coupled to intermediate network 940 and to provider network900. In some embodiments, hardware virtualization service 920 mayprovide one or more APIs 902, for example a web services interface, viawhich a customer network 950 may access functionality provided by thehardware virtualization service 920, for example via a console 994(e.g., a web-based application, standalone application, mobileapplication, etc.). In some embodiments, at the provider network 900,each virtual computing system 992 at customer network 950 may correspondto a computation resource 924 that is leased, rented, or otherwiseprovided to customer network 950.

From an instance of a virtual computing system 992 and/or anothercustomer device 990 (e.g., via console 994), the customer may access thefunctionality of storage service 910, for example via one or more APIs902, to access data from and store data to storage resources 918A-918Nof a virtual data store 916 (e.g., a folder or “bucket”, a virtualizedvolume, a database, etc.) provided by the provider network 900. In someembodiments, a virtualized data store gateway (not shown) may beprovided at the customer network 950 that may locally cache at leastsome data, for example frequently-accessed or critical data, and thatmay communicate with storage service 910 via one or more communicationschannels to upload new or modified data from a local cache so that theprimary store of data (virtualized data store 916) is maintained. Insome embodiments, a user, via a virtual computing system 992 and/or onanother customer device 990, may mount and access virtual data store 916volumes via storage service 910 acting as a storage virtualizationservice, and these volumes may appear to the user as local (virtualized)storage 998.

While not shown in FIG. 9, the virtualization service(s) may also beaccessed from resource instances within the provider network 900 viaAPI(s) 902. For example, a customer, appliance service provider, orother entity may access a virtualization service from within arespective virtual network on the provider network 900 via an API 902 torequest allocation of one or more resource instances within the virtualnetwork or within another virtual network.

Illustrative Systems

In some embodiments, a system that implements a portion or all of thetechniques described herein may include a general-purpose computersystem that includes or is configured to access one or morecomputer-accessible media, such as computer system 1000 illustrated inFIG. 10. In the illustrated embodiment, computer system 1000 includesone or more processors 1010 coupled to a system memory 1020 via aninput/output (I/O) interface 1030. Computer system 1000 further includesa network interface 1040 coupled to I/O interface 1030. While FIG. 10shows computer system 1000 as a single computing device, in variousembodiments a computer system 1000 may include one computing device orany number of computing devices configured to work together as a singlecomputer system 1000.

In various embodiments, computer system 1000 may be a uniprocessorsystem including one processor 1010, or a multiprocessor systemincluding several processors 1010 (e.g., two, four, eight, or anothersuitable number). Processors 1010 may be any suitable processors capableof executing instructions. For example, in various embodiments,processors 1010 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, ARM, PowerPC, SPARC, or MIPS ISAs, or any othersuitable ISA. In multiprocessor systems, each of processors 1010 maycommonly, but not necessarily, implement the same ISA.

System memory 1020 may store instructions and data accessible byprocessor(s) 1010. In various embodiments, system memory 1020 may beimplemented using any suitable memory technology, such as random-accessmemory (RAM), static RAM (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/Flash-type memory, or any other type of memory. In theillustrated embodiment, program instructions and data implementing oneor more desired functions, such as those methods, techniques, and datadescribed above are shown stored within system memory 1020 as databaseservice code 1025 and data 1026.

In one embodiment, I/O interface 1030 may be configured to coordinateI/O traffic between processor 1010, system memory 1020, and anyperipheral devices in the device, including network interface 1040 orother peripheral interfaces. In some embodiments, I/O interface 1030 mayperform any necessary protocol, timing or other data transformations toconvert data signals from one component (e.g., system memory 1020) intoa format suitable for use by another component (e.g., processor 1010).In some embodiments, I/O interface 1030 may include support for devicesattached through various types of peripheral buses, such as a variant ofthe Peripheral Component Interconnect (PCI) bus standard or theUniversal Serial Bus (USB) standard, for example. In some embodiments,the function of I/O interface 1030 may be split into two or moreseparate components, such as a north bridge and a south bridge, forexample. Also, in some embodiments some or all of the functionality ofI/O interface 1030, such as an interface to system memory 1020, may beincorporated directly into processor 1010.

Network interface 1040 may be configured to allow data to be exchangedbetween computer system 1000 and other devices 1060 attached to anetwork or networks 1050, such as other computer systems or devices asillustrated in FIG. 1, for example. In various embodiments, networkinterface 1040 may support communication via any suitable wired orwireless general data networks, such as types of Ethernet network, forexample. Additionally, network interface 1040 may support communicationvia telecommunications/telephony networks such as analog voice networksor digital fiber communications networks, via storage area networks(SANs) such as Fibre Channel SANs, or via I/O any other suitable type ofnetwork and/or protocol.

In some embodiments, a computer system 1000 includes one or more offloadcards 1070 (including one or more processors 1075, and possiblyincluding the one or more network interfaces 1040) that are connectedusing an I/O interface 1030 (e.g., a bus implementing a version of thePeripheral Component Interconnect—Express (PCI-E) standard, or anotherinterconnect such as a QuickPath interconnect (QPI) or UltraPathinterconnect (UPI)). For example, in some embodiments the computersystem 1000 may act as a host electronic device (e.g., operating as partof a hardware virtualization service) that hosts compute instances, andthe one or more offload cards 1070 execute a virtualization manager thatcan manage compute instances that execute on the host electronic device.As an example, in some embodiments the offload card(s) 1070 can performcompute instance management operations such as pausing and/or un-pausingcompute instances, launching and/or terminating compute instances,performing memory transfer/copying operations, etc. These managementoperations may, in some embodiments, be performed by the offload card(s)1070 in coordination with a hypervisor (e.g., upon a request from ahypervisor) that is executed by the other processors 1010A-1010N of thecomputer system 1000. However, in some embodiments the virtualizationmanager implemented by the offload card(s) 1070 can accommodate requestsfrom other entities (e.g., from compute instances themselves), and maynot coordinate with (or service) any separate hypervisor.

In some embodiments, system memory 1020 may be one embodiment of acomputer-accessible medium configured to store program instructions anddata as described above. However, in other embodiments, programinstructions and/or data may be received, sent or stored upon differenttypes of computer-accessible media. Generally speaking, acomputer-accessible medium may include non-transitory storage media ormemory media such as magnetic or optical media, e.g., disk or DVD/CDcoupled to computer system 1000 via I/O interface 1030. A non-transitorycomputer-accessible storage medium may also include any volatile ornon-volatile media such as RAM (e.g., SDRAM, double data rate (DDR)SDRAM, SRAM, etc.), read only memory (ROM), etc., that may be includedin some embodiments of computer system 1000 as system memory 1020 oranother type of memory. Further, a computer-accessible medium mayinclude transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network and/or a wireless link, such as may be implemented vianetwork interface 1040.

Various embodiments discussed or suggested herein can be implemented ina wide variety of operating environments, which in some cases caninclude one or more user computers, computing devices, or processingdevices which can be used to operate any of a number of applications.User or client devices can include any of a number of general-purposepersonal computers, such as desktop or laptop computers running astandard operating system, as well as cellular, wireless, and handhelddevices running mobile software and capable of supporting a number ofnetworking and messaging protocols. Such a system also can include anumber of workstations running any of a variety of commerciallyavailable operating systems and other known applications for purposessuch as development and database management. These devices also caninclude other electronic devices, such as dummy terminals, thin-clients,gaming systems, and/or other devices capable of communicating via anetwork.

Most embodiments utilize at least one network that would be familiar tothose skilled in the art for supporting communications using any of avariety of widely-available protocols, such as Transmission ControlProtocol/Internet Protocol (TCP/IP), File Transfer Protocol (FTP),Universal Plug and Play (UPnP), Network File System (NFS), CommonInternet File System (CIFS), Extensible Messaging and Presence Protocol(XMPP), AppleTalk, etc. The network(s) can include, for example, a localarea network (LAN), a wide-area network (WAN), a virtual private network(VPN), the Internet, an intranet, an extranet, a public switchedtelephone network (PSTN), an infrared network, a wireless network, andany combination thereof.

In embodiments utilizing a web server, the web server can run any of avariety of server or mid-tier applications, including HTTP servers, FileTransfer Protocol (FTP) servers, Common Gateway Interface (CGI) servers,data servers, Java servers, business application servers, etc. Theserver(s) also may be capable of executing programs or scripts inresponse requests from user devices, such as by executing one or moreWeb applications that may be implemented as one or more scripts orprograms written in any programming language, such as Java®, C, C# orC++, or any scripting language, such as Perl, Python, PHP, or TCL, aswell as combinations thereof. The server(s) may also include databaseservers, including without limitation those commercially available fromOracle®, Microsoft®, Sybase®, IBM®, etc. The database servers may berelational or non-relational (e.g., “NoSQL”), distributed ornon-distributed, etc.

Environments disclosed herein can include a variety of data stores andother memory and storage media as discussed above. These can reside in avariety of locations, such as on a storage medium local to (and/orresident in) one or more of the computers or remote from any or all ofthe computers across the network. In a particular set of embodiments,the information may reside in a storage-area network (SAN) familiar tothose skilled in the art. Similarly, any necessary files for performingthe functions attributed to the computers, servers, or other networkdevices may be stored locally and/or remotely, as appropriate. Where asystem includes computerized devices, each such device can includehardware elements that may be electrically coupled via a bus, theelements including, for example, at least one central processing unit(CPU), at least one input device (e.g., a mouse, keyboard, controller,touch screen, or keypad), and/or at least one output device (e.g., adisplay device, printer, or speaker). Such a system may also include oneor more storage devices, such as disk drives, optical storage devices,and solid-state storage devices such as random-access memory (RAM) orread-only memory (ROM), as well as removable media devices, memorycards, flash cards, etc.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.), and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed, and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting, and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services, or other elementslocated within at least one working memory device, including anoperating system and application programs, such as a client applicationor web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets), or both. Further, connection to other computing devicessuch as network input/output devices may be employed.

Storage media and computer readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer readable instructions, data structures,program modules, or other data, including RAM, ROM, ElectricallyErasable Programmable Read-Only Memory (EEPROM), flash memory or othermemory technology, Compact Disc-Read Only Memory (CD-ROM), DigitalVersatile Disk (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by a system device. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

In the preceding description, various embodiments are described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described.

Bracketed text and blocks with dashed borders (e.g., large dashes, smalldashes, dot-dash, and dots) are used herein to illustrate optionaloperations that add additional features to some embodiments. However,such notation should not be taken to mean that these are the onlyoptions or optional operations, and/or that blocks with solid bordersare not optional in certain embodiments.

Reference numerals with suffix letters (e.g., 918A-918N) may be used toindicate that there can be one or multiple instances of the referencedentity in various embodiments, and when there are multiple instances,each does not need to be identical but may instead share some generaltraits or act in common ways. Further, the particular suffixes used arenot meant to imply that a particular amount of the entity exists unlessspecifically indicated to the contrary. Thus, two entities using thesame or different suffix letters may or may not have the same number ofinstances in various embodiments.

References to “one embodiment,” “an embodiment,” “an exampleembodiment,” etc., indicate that the embodiment described may include aparticular feature, structure, or characteristic, but every embodimentmay not necessarily include the particular feature, structure, orcharacteristic. Moreover, such phrases are not necessarily referring tothe same embodiment. Further, when a particular feature, structure, orcharacteristic is described in connection with an embodiment, it issubmitted that it is within the knowledge of one skilled in the art toaffect such feature, structure, or characteristic in connection withother embodiments whether or not explicitly described.

Moreover, in the various embodiments described above, unlessspecifically noted otherwise, disjunctive language such as the phrase“at least one of A, B, or C” is intended to be understood to mean eitherA, B, or C, or any combination thereof (e.g., A, B, and/or C). As such,disjunctive language is not intended to, nor should it be understood to,imply that a given embodiment requires at least one of A, at least oneof B, or at least one of C to each be present.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the disclosure asset forth in the claims.

What is claimed is:
 1. A computer-implemented method comprising:receiving a request at a database service, wherein the request includesa structured query language (SQL) query to be performed on at least aportion of a dataset in the database service and wherein the requestidentifies a machine learning service to be used in processing the SQLquery; creating a virtual operator to perform at least a portion of theSQL query; generating a first batch of machine learning requests basedat least on the portion of the SQL query performed by the virtualoperator; sending the first batch of machine learning requests to aninput buffer of an asynchronous request handler, the asynchronousrequest handler to generate a second batch of machine learning requestsbased on the first batch of machine learning requests; obtaining aplurality of machine learning responses from an output buffer of theasynchronous request handler, the machine learning responses generatedby the machine learning service using a machine learning model inresponse to receiving the second batch of machine learning requests,wherein the machine learning service publishes an applicationprogramming interface (API) to perform inference using the machinelearning model in response to requests received from a plurality ofusers; and generating a query response based on the machine learningresponses.
 2. The computer-implemented method of claim 1, whereingenerating a first batch of machine learning requests based at least onthe SQL query, further comprises: determining a query execution planthat minimizes a number of records associated with machine learningrequest.
 3. The computer-implemented method of claim 1, wherein themachine learning service adds a flag to the output buffer when thesecond batch of machine learning requests has been processed.
 4. Acomputer-implemented method comprising: executing at least a portion ofa query on data stored in a database service using a temporary datastructure to generate a first batch of machine learning requests,wherein the query is a structured query language (SQL) query, whereinthe query identifies a machine learning service using an applicationprogramming interface (API) call to the machine learning service, andwherein the machine learning service publishes the API to performinference using the machine learning model in response to requestsreceived from a plurality of users; generating a second batch of machinelearning requests based on the first batch of machine learning requestsand based on the machine learning service; and obtaining a plurality ofmachine learning responses, the machine learning responses generated bythe machine learning service using a machine learning model in responseto receiving the second batch of machine learning requests.
 5. Thecomputer-implemented method of claim 4, wherein the query identifies themachine learning service using an endpoint associated with the machinelearning model hosted by the machine learning service.
 6. Thecomputer-implemented method of claim 4, wherein the second batch ofmachine learning requests is sent to the machine learning service overat least one network.
 7. The computer-implemented method of claim 4,further comprising: sending a request to the machine learning servicefor the machine learning model; receiving the machine learning modelfrom the machine learning service, the machine learning model compiledfor the database service by the machine learning service; and whereinthe second batch of machine learning requests is sent to the machinelearning model hosted by the database service.
 8. Thecomputer-implemented method of claim 7, further comprising: storing acopy of the machine learning model in a plurality of nodes of thedatabase service, wherein machine learning requests generated during thequery processing by a particular node of the database service are sentto the copy of the machine learning model stored on the particular node.9. The computer-implemented method of claim 4, wherein the second batchsize is different from the first batch size, and wherein the secondbatch size is associated with the machine learning service.
 10. Thecomputer-implemented method of claim 4, wherein the first batch ofmachine learning requests includes machine learning requests generatedin response to multiple queries received from a plurality of differentusers.
 11. A system comprising: a machine learning service implementedby a first one or more electronic devices; and a database serviceimplemented by a second one or more electronic devices, the databaseservice including instructions that upon execution cause the databaseservice to: execute at least a portion of a query on data stored in adatabase service using a temporary data structure to generate a firstbatch of machine learning requests, wherein the query is a structuredquery language (SQL) query, wherein the query identifies the machinelearning service using an application programming interface (API) callto the machine learning service, and wherein the machine learningservice publishes the API to perform inference using the machinelearning model in response to requests received from a plurality ofusers; generating a second batch of machine learning requests based onthe first batch of machine learning requests and based on the machinelearning service; and obtain a plurality of machine learning responses,the machine learning responses generated by the machine learning serviceusing a machine learning model in response to receiving the second batchof machine learning requests.
 12. The system of claim 11, wherein thequery identifies the machine learning service using an endpointassociated with the machine learning model hosted by the machinelearning service.
 13. The system of claim 11, wherein the second batchof machine learning requests is sent to the machine learning serviceover at least one network.
 14. The system of claim 11, furthercomprising: sending a request to the machine learning service for themachine learning model; receiving the machine learning model from themachine learning service, the machine learning model compiled for thedatabase service by the machine learning service; wherein the secondbatch of machine learning requests is sent to the machine learning modelhosted by the database service; and storing a copy of the machinelearning model in a plurality of nodes of the database service, whereinmachine learning requests generated during the query processing by aparticular node of the database service are sent to the copy of themachine learning model stored on the particular node.